How dear are deer volunteers: the efficiency of monitoring deer using teams of volunteers to conduct pellet group counts
Bibliographic record
Abstract
Abstract Deer populations are increasing throughout the northern hemisphere, and unregulated numbers can jeopardize biodiversity and the economy. These populations are difficult to monitor using visual counts. Estimating densities from surveys of faecal pellets is reliable but time-consuming and thus, if carried out by professionals, expensive. Utilizing volunteers has clear advantages. Based on research from the UK (6 years) and Nova Scotia, Canada (4 years), we investigated the methodological refinements and training required to achieve reliable data when using volunteers. For safety reasons volunteers worked in teams of 5–10 (n = 611) under supervision of scientists. We compared faecal accumulation rate and faecal standing crop surveys using 10 × 10 m quadrats. Both methods produced similar estimates of density, but because of significant time savings and greater volunteer enjoyment we favour faecal standing crop over faecal accumulation rate surveys. Volunteer teams surveyed quadrats significantly faster than a single professional but needed significantly longer to reach and stake out new quadrats. On average, teams found 68% of all droppings. Performance of individuals was affected by training, gender, and willingness and aptitude to survey. After five quadrats men scored significantly higher than women but this difference was reduced after 20 quadrats. Age did not affect performance but willingness and aptitude correlated with ability to find and identify droppings. We conclude that volunteers can monitor deer effectively but that techniques should be modified. The provision of context, training, supervision and verification by a professional are essential. Because of the drain on scientists’ time, cost-effective volunteer deployment is a question of scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".